Abstract
Dyslexia, a neurodevelopment condition impacting reading and language abilities, presents notable difficulties in promptly identifying and implementing effective interventions Traditional methods for diagnosing dyslexia often rely on subjective assessments and standardized tests, leading to delays in recognition and support. This paper offers an extensive examination of how deep learning techniques are applied in the domain of detecting and intervening in dyslexia. The integration of deep learning algorithms into dyslexia research offers promising avenues for more accurate and timely identification of individuals at risk. By leveraging neural networks and advanced machine learning models, researchers have begun to explore novel approaches that analyze linguistic patterns, eye-tracking data, brain imaging, and behavioral markers associated with dyslexia. Furthermore, this paper discusses the potential of deep learning in tailoring personalized interventions for individuals with dyslexia. These interventions aim to adapt to the specific learning needs of each individual, providing targeted support and enhancing the effectiveness of remediation strategies. While highlighting the advancements made in utilizing deep learning for dyslexia, this review also addresses challenges, including data scarcity, model interpretability, and ethical considerations. Additionally, it proposes future research directions that emphasize collaborative efforts among researchers, educators, and technology developers to foster the development of robust and accessible tools for dyslexia assessment and intervention.
